This proposal describes the development and evaluation of a rule-based inductive machine-learning system to aid in the development of clinical decision-support aids from data. This system will be specifically designed to allow a clinical expert to add domain knowledge to improve the learning process. Physicians often use rules as clinical guidelines in their everyday practice. Because individual rules are so easily understood, we believe that a rule-based system is ideally suited to the enhancement of domain knowledge for the learning of clinical decision-support systems. Many clinical databases are sufficiently small and/or biased as to prevent current statistical and machine-learning methods from producing optimal clinical decision-support aids. We believe this system will improve the learning of rule-based models for clinical decision-support, especially from these inadequate databases. A secondary aim of this project is to improve on a current rule-based, inductive machine-learning system by adding global ruleset evaluation metrics to the rule learning process.